U.S. patent application number 14/421698 was filed with the patent office on 2015-08-06 for virtual manager.
This patent application is currently assigned to EVERSEEN LIMITED. The applicant listed for this patent is EVERSEEN LIMITED. Invention is credited to Gavin Doyle, Alan O'Herlihy.
Application Number | 20150221191 14/421698 |
Document ID | / |
Family ID | 49684411 |
Filed Date | 2015-08-06 |
United States Patent
Application |
20150221191 |
Kind Code |
A1 |
Doyle; Gavin ; et
al. |
August 6, 2015 |
VIRTUAL MANAGER
Abstract
A virtual management system comprises video cameras, and various
other sensors that acquire event data indicative relating to the
processing of stock. This data is passed to a local data collection
device that aggregates the event data and passes it via a network
to a number of remote data processing modules. The event data is
allocated to each of the data processing modules based upon their
assigned tasks by a virtual manager agent. A data processing module
receives the aggregated event data from the local data collection
device via a network and processes the event data according to a
set of pre-defined rules. The data processing module generates an
alert in response to the processing of the event data indicating
that a pre-defined event has occurred, and transmits the alert to a
remote device associated with an employee.
Inventors: |
Doyle; Gavin; (Ovens,
IE) ; O'Herlihy; Alan; (Blarney, IE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
EVERSEEN LIMITED |
Blackpool, Cork |
|
IE |
|
|
Assignee: |
EVERSEEN LIMITED
Blackpool, Cork
IE
|
Family ID: |
49684411 |
Appl. No.: |
14/421698 |
Filed: |
August 15, 2013 |
PCT Filed: |
August 15, 2013 |
PCT NO: |
PCT/EP2013/067093 |
371 Date: |
February 13, 2015 |
Current U.S.
Class: |
340/568.1 |
Current CPC
Class: |
G06Q 10/08 20130101;
G07G 1/14 20130101; G08B 13/00 20130101; G06Q 10/06 20130101; G07F
19/207 20130101; G07G 3/003 20130101; G06Q 10/0639 20130101 |
International
Class: |
G08B 13/00 20060101
G08B013/00 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 15, 2012 |
IE |
S2012/0354 |
Claims
1-54. (canceled)
55. A virtual management system data processing unit comprising: at
transceiver arranged to control the flow of data to and from the
data processing unit; a processor arranged to receive, via the
transceiver at least a portion of event data acquired from at least
one local data acquisition device; the processor being further
arranged process the event data according to a set of pre-defined
rules, being arranged to generate an alert in response to the
processing of the event data indicating that a specified event has
occurred, and being further arranged to transmit the alert to a
remote device associated with an employee, via the transceiver.
56. The virtual management system data processing unit according to
claim 55 wherein the rules comprise at least one escalation rule
related to the escalation of the alert through a management
hierarchy.
57. The virtual management system data processing unit according to
claim 56, wherein the at least one escalation rule is related to
escalating the alert based upon at least one of the following: a
delay in entering a response to the alert at a, or the, remote
device, an increased frequency of the event, the reoccurrence of
the event.
58. The virtual management system data processing unit according to
claim 56, wherein the at least one escalation rule may be related
to at least one of the following: a particular retail unit, a group
of retail units, a geographical area, a person, a time period.
59. The virtual management system data processing unit according to
claim 55 wherein the rules are arranged to identify clusters of
events.
60. The virtual management system data processing unit according to
claim 59 wherein the clusters of events may be geographically
linked, temporally linked and/or linked to one or more persons.
61. The virtual management system data processing unit according to
claim 55 wherein the rules comprise dynamically variable rules.
62. The virtual management system data processing unit according to
claim 61 wherein the dynamically variable rules comprise
machine-learning algorithms.
63. The virtual management system data processing unit according to
claim 55 wherein the processor is arranged to update the rules in
response to event data.
64. The virtual management system data processing unit according to
claim 63 wherein the processor is arranged to compare an event
identified by the rules to stored model event data.
65. The virtual management system data processing unit according to
claim 64 wherein the processor is arranged to update parameters
associated with the model event data in response to the
comparison.
66. The virtual management system data processing unit according to
claim 65 wherein the processor is arranged to selectively generate
the alert based upon the comparison.
67. The virtual management system data processing unit according to
claim 63 wherein the processor has an instance of a virtual manager
agent associated with a retail store running thereupon, the virtual
manager agent being arranged to control the application of the
rules and the generation of the alert.
68. The virtual management system data processing unit according to
claim 67 wherein the virtual manager agent is arranged to control
the escalation of the alert through a management hierarchy.
69. The virtual management system data processing unit according to
claim 67 wherein the virtual manager agent is run on the at least
one data processing module, or is instantiated across a plurality
of the data processing modules.
70. A method of virtually managing stock comprising: acquiring
event data indicative of an event at at least one local data
acquisition device; aggregating the event data at a local data
collection device; receiving the aggregated event data from the
data collection device via a network; processing the event data
according to a set of pre-defined rules; generating an alert in
response to the processing of the event data indicating that a
pre-defined event has occurred at the at least one data processing
module; and transmitting the alert to a remote device associated
with an employee.
71. The method of claim 70 further comprising escalating of the
alert through a management hierarchy.
72. The method of claim 70 further comprising escalating the alert
based upon at least one of the following: a delay in entering a
response to the alert at a, or the, remote device, an increased
frequency of the event, the reoccurrence of the event.
73. The method of claim 70 further comprising escalating the alert
based upon at least one of the following: a particular retail unit,
a group of retail units, a geographical area, a person, a time
period.
74. The method according to claim 70 further comprising identifying
clusters of events.
75. The method according to claim 74 wherein clusters are
geographically linked, temporally linked and/or linked to one or
more persons.
76. The method according to claim 70 further comprising varying the
rules dynamically.
77. The method according to claim 76 wherein the dynamically
variable rules may comprise machine-learning algorithms.
78. The method according to 70 comprising comparing an event
identified by the rules to stored model event data.
79. The method according to claim 78 further comprising updating
parameters associated with the model event data in response to the
comparison.
80. The method according to claim 78 comprising selectively
generating the alert based upon the comparison.
81. The method according to claim 70 further comprising identifying
a correlation between POS event data and video event data that
corresponds to an indication of a fraudulent transaction.
82. The method according to claim 70 further comprising identifying
a correlation between POS event data, video event data and input
from at least one sensor and/or other input device that corresponds
to an indication of a fraudulent transaction.
83. The method according to claim 70 comprising instantiating an
instance of a virtual manager agent associated with a particular
retail store, the virtual manager agent being arranged to control
the application of the rules and the generation of the alert.
84. The method according to claim 83 comprising controlling the
escalation of the alert through a management hierarchy via the
virtual manager agent.
85. The method according to claim 83 comprising instantiating a
portion a virtual manager agent associated with a particular retail
store on the at least one data processing module.
86. A computer readable storage medium carrying a computer program
stored thereon which when executed cause a processor to: acquire
event data indicative of an event at at least one local data
acquisition device; aggregate the event data at a local data
collection device; receive the aggregated event data from the data
collection device via a network; process the event data according
to a set of pre-defined rules; generate an alert in response to the
processing of the event data indicating that a pre-defined event
has occurred at the at least one data processing module; and
transmit the alert to a remote device associated with an
employee.
87. The computer readable storage medium of claim 86 further
comprising escalating of the alert through a management
hierarchy.
88. The computer readable storage medium of claim 86 further
comprising escalating the alert based upon at least one of the
following: a delay in entering a response to the alert at the
remote device, an increased frequency of the event, or the
reoccurrence of the event.
89. The computer readable storage medium of claim 86 further
comprising escalating the alert based upon at least one of the
following: a particular retail unit, a group of retail units, a
geographical area, a person, a time period.
90. The computer readable storage medium according to claim 86
further comprising identifying clusters of events.
91. The computer readable storage medium according to claim 90
wherein clusters are geographically linked, temporally linked
and/or linked to one or more persons.
92. The computer readable storage medium according to claim 86
further comprising varying the rules dynamically.
93. The computer readable storage medium according to claim 92
wherein the dynamically variable rules may comprise
machine-learning algorithms.
94. The computer readable storage medium according to claim 86
comprising comparing an event identified by the rules to stored
model event data.
95. The computer readable storage medium according to claim 94
further comprising updating parameters associated with the model
event data in response to the comparison.
96. The computer readable storage medium according to claim 94
comprising selectively generating the alert based upon the
comparison.
97. The computer readable storage medium according to claim 86
further comprising identifying a correlation between POS event data
and video event data that corresponds to an indication of a
fraudulent transaction.
98. The computer readable storage medium according to claim 86
further comprising identifying a correlation between POS event
data, video event data and input from at least one sensor and/or
other input device that corresponds to an indication of a
fraudulent transaction.
99. The computer readable storage medium according to claim 86
comprising instantiating an instance of a virtual manager agent
associated with a particular retail store, the virtual manager
agent being arranged to control the application of the rules and
the generation of the alert.
100. The computer readable storage medium according to claim 99
comprising controlling the escalation of the alert through a
management hierarchy via the virtual manager agent.
101. The method according to claim 99 comprising instantiating a
portion a virtual manager agent associated with a particular retail
store on the at least one data processing module.
102. A virtual management system comprising: at least one data
acquisition device arranged to acquire event data indicative of an
event at a location; a local data collection device arranged to
aggregate the event data; at least one data processing module
arranged to receive the aggregated event data from the local data
collection device via a network, and being further arranged to
process the event data according to a set of pre-defined rules; the
at least one data processing module being further arranged to
generate an alert in response to the processing of the event data
indicating that a predefined event has occurred, and being further
arranged to transmit the alert to a remote device associated with
an employee.
103. The virtual management system according to claim 102 wherein
the rules comprise at least one escalation rule related to the
escalation of the alert through a management hierarchy.
104. The virtual management system according to claim 102 wherein
the at least one escalation rule is related to escalating the alert
based upon at least one of the following: a delay in entering a
response to the alert at the remote device an increased frequency
of the event, or the reoccurrence of the event.
105. The virtual management system according to claim 102 wherein
the at least one escalation rule is related to at least one of the
following: a particular retail unit, area within a retail unit, a
group of retail units, a geographical area, a person, group of
persons, a relationship between persons, a time period.
106. The virtual management system according to claim 102 wherein
the rules are arranged to identify clusters of events.
107. The virtual management system according to claim 106 wherein
the clusters are geographically linked, temporally linked,
technologically linked, and/or linked to one or more persons,
and/or one or more business factors said business factors selected
from a list including footfall, spend per customer, customer
satisfaction, type of goods purchased, queue management,
merchandising management, hot spot/cold spot metrics, customer
theft identification, trolley and basket loss identification,
company rules and procedure violation identification, cash room
irregular activity identification, customer accident
identification, employee accident identification or in store safety
hazard identification.
108. The virtual management system according to claim 102 wherein
the rules comprise dynamically variable rules.
109. The virtual management system according to claim 108 wherein
the dynamically variable rules comprise machine-learning
algorithms.
110. The virtual management system according to claim 102 wherein
the at least one data processing module is arranged to compare an
event identified by the rules to stored model event data.
111. The virtual management system according to claim 110 wherein
the at least one processing module may be arranged to update
parameters associated with the model event data in response to the
comparison.
112. The virtual management system according to claim 110 wherein
the at least one processing module may be arranged to selectively
generate the alert based upon the comparison.
113. The virtual management system according to claim 102 wherein
the at least one data acquisition device comprises at least one of
the following: a POS terminal, a video camera, a radio-frequency
identification, RFID tag, an electronic price label, EPL, a
location sensor, an audio sensor, an accelerometer, a magnetometer,
an electrometer, an electro-optical sensor, a tactile sensor, a
piezoelectric sensor, a heat sensor, a proximity sensor, any other
suitable sensor for detecting any of the following: position,
speed, heat, presence of an object, position, angle, distance,
displacement, electrical field, electromagnetic field,
gravitational field, force, density, direction, flow properties,
for example but not limited to, flow of people, chemical sensor,
environmental sensor, for example but not limited to weather
sensor.
114. The virtual management system according to claim 102 wherein
the at least one data processing module comprises a rule arranged
to identify a correlation between POS physical event data and video
event data which corresponds to an indication of a fraudulent
transaction.
115. The virtual management system according to claim 102 wherein
the at least one data processing module comprises a rule arranged
to identify a correlation between POS physical event data, video
event data and input from at least one sensor and/or other input
device, which corresponds to an indication of a fraudulent
transaction.
116. The virtual management system according to claim 115 wherein
the physical event data includes POS transaction data.
117. The virtual management system according to claim 102 wherein
the system comprises a plurality of data processing modules.
118. The virtual management system according to claim 117 wherein
the plurality of data processing modules may be distributed
geographically.
119. The virtual management system according to claim 117 wherein
differing data processing modules are arranged to process different
portions of the aggregated event data.
120. The virtual management system according to claim 119 wherein
differing portions of the event data relate to differing event
types.
121. The virtual management system according to claim 102 wherein
each location has an instance of a virtual manager agent associated
with it, the virtual manager agent being arranged to control the
application of the rules and the generation of the alert.
122. The virtual management system according to claim 121 wherein
the virtual manager agent is arranged to control the escalation of
the alert through the management hierarchy.
123. The virtual management system according to claim 122 wherein
the virtual manager agent is run on the at least one data
processing module, or instantiated across a plurality of the data
processing modules.
Description
TECHNICAL FIELD
[0001] The present invention relates to a virtual manager. More
specifically, but not exclusively, it relates to a retail virtual
manager. Even more specifically, but not exclusively, it relates to
an intelligent remote retail virtual manager.
BACKGROUND ART
[0002] Retailers encounter a large number of factors which server
to reduce their profitability, in particular their gross margin.
One non-limiting example of factors which adversely affect
profitability, include "sweethearting" where a customer pays for a
low value item whilst purchasing a high value item with the
collusion of the checkout assistant, or at a self-service checkout
terminal. Another non-limiting example is where the layout of a
retail unit is such that it is not customer friendly, resulting in
poor sales of stock items that would otherwise realise higher sales
in an alternative position within the retail unit.
[0003] In order to address these issues it is common practice to
employ a team of managers to cover all opening hours of a retail
unit. This practice is expensive for the retail unit owner and does
not address the case where one, or more, of the team of managers is
indulging in the fraudulent activity and is therefore not likely to
address the fraudulent activity.
[0004] Attempts have been made to correlate point-of-sale (POS)
terminal outputs with video surveillance footage in order to
identify fraudulent activity such as sweethearting, see for example
U.S. Pat. No. 7,631,808 B (STOPLIFT, INC). However, these POS-video
correlations merely identify fraudulent activity and do not add
further value to the retailer, there is no attempt to further
increase the gross margin associated with a retail unit by
identifying further issues with, for example the retail unit's
layout.
[0005] Additionally, the prior art solutions identify that a
problem has occurred but do not automatically identify the
reoccurrence of problem that may be indicative of a failure of a
manager to address the issue(s).
[0006] Naturally, the cost and complexity of addressing gross
margin issues increases with the estate of the retailer, for
example a large retailer may divide their estate into regions under
regional managers reporting in to an overall manager who reports to
the chief executive. The present attempts to identify fraudulent
transactions do not address how to escalate notification of
problems to the appropriate person within retailer, for example the
escalation from a regional manager to a general manager if a
problem is seen to be recurrent within a particular region, but not
in other regions. Thus, in prior art systems there is no
correlation between the nature and occurrence of an issue and its
escalation through the retailer's organisational hierarchy.
DISCLOSURE OF INVENTION
[0007] According to a first aspect of the present invention there
is provided a virtual management system comprising: at least one
data acquisition device arranged to acquire event data indicative
of an event at a location; a local data collection device arranged
to aggregate the event data; at least one data processing module
arranged to receive the aggregated event data from the local data
collection device via a network, and being further arranged to
process the event data according to a set of pre-defined rules; the
at least one data processing module being further arranged to
generate an alert in response to the processing of the event data
indicating that a predefined event has occurred, and being further
arranged to transmit the alert to a remote device associated with
an employee.
[0008] The rules may comprise at least one escalation rule related
to the escalation of the alert through a management hierarchy. The
at least one escalation rule may be related to escalating the alert
based upon at least one of the following: a delay in entering a
response to the alert at a, or the, remote device, an increased
frequency of the event, the reoccurrence of the event. The at least
one escalation rule may be related to at least one of the
following: a particular retail unit, area within a retail unit, a
group of retail units, a geographical area, a person, group of
persons, a relationship between persons, a time period.
[0009] The rules may be arranged to identify clusters of events.
The clusters may be geographically linked, temporally linked,
technologically linked, and/or linked to one or more persons,
and/or one or more business factors. Business factors may include,
by way of non-limiting example only, footfall, spend per customer,
customer satisfaction, type of goods purchased.
[0010] The rules may comprise dynamically variable rules. The
dynamically variable rules may comprise machine-learning
algorithms.
[0011] The at least one data processing module may be arranged to
compare an event identified by the rules to stored model event
data. The at least one processing module may be arranged to update
parameters associated with the model event data in response to the
comparison. The at least one processing module may be arranged to
selectively generate the alert based upon the comparison.
[0012] The at least one data acquisition device comprises at least
one of the following: a POS terminal, a video camera, a
radio-frequency identification (RFID) tag, an electronic price
label (EPL), a location sensor, an audio sensor, an accelerometer,
a magnetometer, an electrometer, an electro-optical sensor, a
tactile sensor, a piezoelectric sensor, a heat sensor, a proximity
sensor, any other suitable sensor for detecting any of the
following: position, speed, heat, presence of an object, position,
angle, distance, displacement, electrical field, electromagnetic
field, gravitational field, force, density, direction, flow
properties, for example but not limited to, flow of people,
chemical sensor, environmental sensor, for example but not limited
to weather sensor.
[0013] The at least one data processing module may comprise a rule
arranged to identify a correlation between POS physical event data
and video event data which corresponds to an indication of a
fraudulent transaction. The at least one data processing module may
comprise a rule arranged to identify a correlation between POS
physical event data, video event data and input from at least one
sensor and/or other input device, which corresponds to an
indication of a fraudulent transaction. Physical event data may
include, by way of non-limiting example only, POS transaction
data.
[0014] The system may comprise a plurality of data processing
modules. The plurality of data processing modules may be
distributed geographically. Differing data processing modules may
be arranged to process different portions of the aggregated event
data. Typically, the differing portions of the event data may
relate to differing event types.
[0015] Each location has an instance of a virtual manager agent
associated with it, the virtual manager agent being arranged to
control the application of the rules and the generation of the
alert. The virtual manager agent may be arranged to control the
escalation of the alert through the management hierarchy. The
virtual manager agent may be run on the at least one data
processing module, or it may be instantiated across a plurality of
the data processing modules.
[0016] According to a second aspect of the present invention there
is provided a virtual management system data processing unit
comprising: a transceiver arranged to control the flow of data to
and from the data processing unit; a processor arranged to receive,
via the transceiver at least a portion of event data acquired from
at least one data acquisition device; the processor being further
arranged to process the event data according to a set of
pre-defined rules, being arranged to generate an alert in response
to the processing of the event data indicating that a predefined
event has occurred, and being further arranged to transmit the
alert to a remote device associated with an employee, via the
transceiver.
[0017] The rules may be stored locally at a storage device of the
processing unit.
[0018] The rules may comprise at least one escalation rule related
to the escalation of the alert through a management hierarchy. The
at least one escalation rule may be related to escalating the alert
based upon at least one of the following: a delay in entering a
response to the alert at a, or the, remote device, an increased
frequency of the event, the reoccurrence of the event. The at least
one escalation rule may be related to at least one of the
following: a particular retail unit, a group of retail units, a
geographical area, a person, a time period.
[0019] The rules may be arranged to identify clusters of events.
The clusters may be geographically linked, temporally linked and/or
linked to one or more persons.
[0020] The rules may comprise dynamically variable rules. The
dynamically variable rules may comprise machine-learning
algorithms. The processor may be arranged to update the rules in
response to event data.
[0021] The processor may be arranged to compare an event identified
by the rules to stored model event data. The processor may be
arranged to update parameters associated with the model event data
in response to the comparison. The processor may be arranged to
selectively generate the alert based upon the comparison.
[0022] The processor may have an instance of a virtual manager
agent associated with a retail store running thereupon, the virtual
manager agent being arranged to control the application of the
rules and the generation of the alert. The virtual manager agent
may be arranged to control the escalation of the alert through a
management hierarchy. The virtual manager agent may be run on the
at least one data processing module, or it may be instantiated
across a plurality of the data processing modules.
[0023] According to a third aspect of the present invention there
is provided a method of managing a retail store virtually
comprising: acquiring event data indicative of an event within the
retail store at at least one data acquisition device; receiving
aggregated event data from a data collection device at a data
processing module via a network; processing the event data
according to a set of pre-defined rules; generating an alert in
response to the processing of the event data indicating that a
predefined event has occurred at the at least one data processing
module; and transmitting the alert to a remote device associated
with an employee.
[0024] The method may further comprise escalating of the alert
through a management hierarchy. The method may further comprise
escalating the alert based upon at least one of the following: a
delay in entering a response to the alert at a, or the, remote
device, an increased frequency of the event, the reoccurrence of
the event. The method may further comprise escalating the alert
based upon at least one of the following: a particular retail unit,
a group of retail units, a geographical area, a person, a time
period.
[0025] The method may comprise identifying clusters of events. The
clusters may be geographically linked, temporally linked and/or
linked to one or more persons.
[0026] The method may comprise varying the rules dynamically. The
dynamically variable rules may comprise machine-learning
algorithms.
[0027] The method may comprise comparing an event identified by the
rules to stored model event data. The method may comprise updating
parameters associated with the model event data in response to the
comparison. The method may comprise selectively generating the
alert based upon the comparison.
[0028] The method may comprise identifying a correlation between
POS event data and video event data that corresponds to an
indication of a fraudulent transaction. The method may comprise
identifying a correlation between POS event data, video event data
and input from at least one sensor and/or other input device that
corresponds to an indication of a fraudulent transaction.
[0029] The method may comprise instantiating an instance of a
virtual manager agent associated with a particular retail store,
the virtual manager agent being arranged to control the application
of the rules and the generation of the alert. The method may
comprise controlling the escalation of the alert through a
management hierarchy via the virtual manager agent. The method may
comprise instantiating a portion a virtual manager agent associated
with a particular retail store on the at least one data processing
module.
[0030] According to a fourth aspect of the present invention there
is provided software, which when executed upon a processor, causes
the processor to act as the processor of the processing unit of the
second aspect of the present invention.
[0031] According to a fifth aspect of the present invention there
is provided a retail manager agent which, when executed upon a
processor, causes the processor to act as the retail management
unit of any one of the first, second or third aspects of the
present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0032] The invention will now be described, by way of example only,
with reference to the accompanying drawings, in which:
[0033] FIG. 1 is a schematic diagram of an embodiment of virtual
retail management system in accordance with at least one aspect of
the present invention;
[0034] FIG. 2 is a schematic diagram of a store deploying the
virtual retail management system of FIG. 1; and
[0035] FIG. 3 is a flow chart detailing an embodiment of a method
of managing a retail store virtually in accordance with another
aspect of the present invention.
DETAILED DESCRIPTION
[0036] Referring now to FIGS. 1 and 2, a virtual retail management
system 100 comprises a plurality of stores 102a-d each having a
respective virtual manager agent 104a-d associated with it, a
network 106, a number of data analysis modules 108a-c and a number
of remote terminals 110a-d associated with employees of a retailer
who owns the stores 102a-d. Typically, a remote terminal 110a-d
will comprise a mobile telephone, a tablet, a PC or a laptop.
[0037] Each of the stores 102a-d comprises a closed circuit
television (CCTV) system 112 and at least one POS terminal 114.
Data acquired from the CCTV system 112 and the POS terminal 114 is
fed to an in-store data aggregator 116. The aggregator 116 collates
all video and POS data relating to transactions. Additionally, or
alternatively, the aggregator 116 collects data relating to
stocking levels of shelves, EPL and/or RFID data relating to prices
and sales of goods. In at least one embodiment, the correlation of
video data from areas of the store 102 along any one or combination
of stocking levels, EPL or RFID data allows for the interdependence
of sales and store layout to be monitored by subsequent data
processing of this data for correlations.
[0038] Each data analysis module 108a-c comprises a transceiver
117, a processor 118 and a rules database 120. The processor 118 of
at least one of the data analysis module 108a-c runs a respective
store retail manager agent 122a-d for each store 102a-d, and data
analysis application 124. Additionally, or alternatively, the
processor 118 runs a machine-learning algorithm 126. It will be
appreciated that in some embodiments the rules database 120
resident on each data analysis module 108a-c may be a direct copy
of that present on at least one other data analysis module, or it
may be tailored for a particular aspect of data analysis.
[0039] It will be appreciated that there is typically one retail
management agent 122a-d for each store and this may be located on
one of the data analysis modules 108a-c that controls the
processing of the aggregated data across the data analysis modules
108a-c. Alternatively, the retail management agent 122a-d can be
distributed across the data analysis modules 108a-c.
[0040] In use, the aggregated data is received at one of the data
analysis modules 108a-c where the retail management agent 122
assigns parts or all of the aggregated data to the data analysis
modules 108a-c for analysis. In at least one embodiment, the
machine learning algorithm 126 analyses the aggregated data for any
previously unknown patterns within the data, or for patterns that
deviate slightly from those already defined in the rules database
120. The machine learning algorithm 126 records these data patterns
for incorporation into the rules database 120, should the data
pattern be identified as corresponding to an event that is to be
monitored in the future.
[0041] The processor 118 runs an incident analysis routine that
analyses the collated POS and CCTV data in order to establish
patterns that correspond to an incident. Typically, the incident
analysis routine is a video content analysis routine. In one
non-limiting example, the change in movement of a scanned item
associated with a "sweethearting" within CCTV data can be
cross-referenced with the scan of a low value item at a POS to
determine that an event of "sweethearting" is likely to occur. The
rules database 120 is accessed during this analysis such that any
number of models of stored event types can be compared to the data
to provide a rich analysis of the data beyond merely identifying
"sweethearting".
[0042] In at least one embodiment, the retail management agent 122
is provided with data structures which details for example any of
the following the management structure, staff rosters, layout,
stock levels and historical sales data of each store 102a-d. This
allows, for example, an analysis to be carried out as to which
employees are present when an event occurs and/or which areas of
the store 102a-d are most susceptible to stock loss etc.
[0043] The above detailed usage of the system provides an overview
of the situation of a single store. However, in many retail
operations the estate extends over a multiplicity of store
locations, for example the four stores of FIG. 1 may be divided
into two regions. The processor 118 receives processed data
relating to each store 102a-d from its respective retail management
agent 122 and runs an intelligent organisational modelling (IOM)
routine in relation to the processed data. The IOM routine collates
all of the processed data to establish patterns within it, for
example the stores 102a,b which form Region 1 may show a high
incidence of "sweethearting", whilst the stores 102c,d which form
Region 2 may not. However, for example, the stores of Region 2
102c,d may show high proportions of stock loss of alcoholic
beverages where as the stores of Region 1 show none. Such regional,
national or even store level patterns can be established. The
establishment of these patterns allows for their inclusion into
further rules to be introduced into the rules database once the
cause of these patterns has been correlated to an action or type of
incident. For example, inner city stores may be found to have a
higher incidence of stock loss due to theft and rural stores may be
more prone to "sweethearting". Once established as such the
relative thresholds for flagging the two incidents in the
respective types of stores can be accurately and intelligently set
by the software, and dynamically monitored and altered by the
software as more data becomes available over time to improve the
accuracy of detection.
[0044] Once the IOM routine has analysed the data it generates
output alerts that are to be sent to a data communication elevator
(DCE) 128 which128, which is also resident upon the data analysis
modules 108a-c. The DCE 128 contains a detailed breakdown of the
retailer's management hierarchy 130. The DCE 128 determines which
level of management should be informed of an incident dependent
upon, for example the severity of the incident. For example, a
single instance of "sweethearting" may be deemed suitable for
reporting to a store manager 130a, in order that they can deal with
it. However, a repeated instance of stock loss from a storeroom may
be considered suitable for reporting to a regional manager 130b, as
it cannot be guaranteed that a store duty manager, or overall
manager was not complicit. In an extreme case, the DCE 128 may
elevate an alert directly to the chief executive officer, or owner,
130d of the retail group.
[0045] Furthermore, the DCE 128 actively retains historic data and
compares real-time data with such historic data. This allows for
trends in incidents to be established and for appropriate elevation
or demotion of the level of management hierarchy 130 to which an
alert is directed. For example, the failure to address an issue
that is prevalent in a region by a regional manager may be
escalated to an operations manager 130c. Conversely, where a
regional manager 130b was being sent alerts related to an issue
within in his area alerts relating to this issue can be demoted to
local managers 130a, where there are only localised instances of
the issue occurring, indicating that the regional problem has been
adequately addressed.
[0046] Once the correct level of management hierarchy has been
addressed an alert is issued to the remote device associated with
the manager concerned via the transceiver 117 and a suitable
network. For example, for a mobile telephone a GSM, CDMA or UTMS
network can be employed and for a laptop etc., the Internet and
where appropriate a wireless network.
[0047] Referring now to FIG. 3, a method of managing a retail store
virtually comprises acquiring event data indicative of an event
within the retail store 102a-d from a CCTV system 112 and a POS
terminal 114 (Step 300). The aggregated event data is received from
an in-store data collection at a data processing module 108a-c via
a network (Step 302). The event data is processed according to a
set of pre-defined rules (Step 304). An alert is generated in
response to the processing of the event data indicating that a
specified event has occurred at the data processing module 108a-c
(Step 306). The alert is transmitted to a remote device associated
with an employee associated with the retail store 102a-d (Step
308).
[0048] In at least one embodiment, the method comprises escalating
of the alert through a retailer's management hierarchy prior to its
being sent.
[0049] It will be appreciated that although described with
reference to "rules" the "rules" may be applied in the form of any
of the following: threshold, frequency, and/or decision making
algorithms.
[0050] It will be appreciated that the term "employee" as used
herein is intended to encompass a business owner or any third party
granted access to the output of the virtual retail management
system described herein.
[0051] Typically, each module comprises a processor to enable the
module to perform its function, and a communications facility to
enable the module to communicate with outside entities, but in some
instances this may not be essential.
[0052] It will also be appreciated that the steps of the methods
described herein may be carried out in any suitable order, or
simultaneously where appropriate. The methods described herein may
be performed by software in machine-readable form on a tangible
storage medium or as a propagating signal.
[0053] Various modifications may be made to the above described
embodiment without departing from the spirit and the scope of the
invention.
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